The DOI matches its Crossref record; 158 of the 175 references it cites were found in a registry or an index, and 17 are not checked yet.
The use of machine learning algorithms in recommender systems: A systematic review
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The mathematics of recommender systems [20] and interpolation capabilities of machine learning algorithms will enable proper recommendations on making the secondary surveys cheaper with a minor loss in the value of acquired information.”
From Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future (2021) · cites this paper as [20]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476 · cited 1×
“The MapReduce algorithm [17], which became popular in mid-1990s, is a programming model suited for distributed and parallel computation.”
MethodJain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,925 · cited 1×
“Today, there is a plethora of ML algorithms (k-nearest neighbor [48], clustering [28], Bayes network [22], to name a few types), which are used in applications that range from vacuum cleaner robots [13] and assistance for disabled people [30] to pattern recognition in images [63], or self-driving…”
BackgroundAdomavičius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,539 · cited 2×
“Recommender systems (RS) are used to help users find new items or services, such as books, music, transportation or even people, based on information about the user, or the recommended item [2].”
BackgroundSoftware engineering: A practitioner's approach
(1983) · Advances in Engineering Software (1978) · doi:10.1016/0141-1195(83)90118-3 · Cited by 8,227
(2004) · Cited by 5,198
The Hadoop Distributed File System
Shvachko, K. V., Kuang, H., Radia, S., & Chansler, R. J. (2010) · Digest of papers - IEEE Symposium on Mass Storage Systems · doi:10.1109/msst.2010.5496972 · Cited by 4,911 · cited 1×
“The proposed architecture that provides on demand tools for analysis uses the storage technologies HDFS (Hadoop Distributed File System) (Shvachko et al., 2010) and HBase6 for persistence logs and structured information about the execution and predictions.”
MethodFriedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742 · cited 1×
“Today, there is a plethora of ML algorithms (k-nearest neighbor [48], clustering [28], Bayes network [22], to name a few types), which are used in applications that range from vacuum cleaner robots [13] and assistance for disabled people [30] to pattern recognition in images [63], or self-driving…”
BackgroundSupervised Machine Learning: A Review of Classification Techniques
Kotsiantis, S. B. (2007) · Informatica · Cited by 4,192
Using collaborative filtering to weave an information tapestry
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. B. (1992) · Communications of the ACM · doi:10.1145/138859.138867 · Cited by 4,071 · cited 1×
“The field of RS has its origins in the mid-1990s with the introduction of Tapestry [23], the first RS.”
BackgroundUnderstanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
Research works printed but not linked: 14
- Aouay, 2014, 2014, 523
- Bagchi, 2015, 50, 229
- Burhans, 2004, 1, 82
- Jin, 2005, 1, 213
- Jun, 2005, 3512, 349
- Li, 2008, 3, 1626
- Liu, 2011, 4, 1909
- Montañés, 2009, 497, 173
- Murfi, 2009, 497, 201
- Nicol, 2014, 2, 1390
- Pecli, 2015, 1, 295
- Yeh, 2010, 1, 209
- Zhang, 2007, 6, 3506
- Zhang, 2007, 7, 4113
Cited by
Sorted by citations
Items 201–300 of 742
Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda
von der Weth, C., Abdul, A., Fan, S., & Kankanhalli, M. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414692 · Cited by 17
“result in “over-customization” are recommender systems based on collaborative filtering [14, 17, 80].”
BackgroundJamin, A., Abraham, P., & Humeau‐Heurtier, A. (2020) · Clinical Physiology and Functional Imaging · doi:10.1111/cpf.12686 · Cited by 16
“It is a multidisciplinary area that is used in different fields: bank, logistics, medicine, trade, … (Barboza et al., 2017; Portugal et al., 2017) In medicine, predictive data analytics is a crucial challenge to improve the diagnosis and the monitoring of patients.”
BackgroundPredictive System of Semiconductor Failures based on Machine Learning Approach
Mourabit, Y. E., El, Y., Zougagh, H., & Wadiai, Y. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0111225 · Cited by 16
“Secondly, for every dataset, we applied four machine learning algorithms (SVM, NB, MLP, C4.5) [22], and perform the efficiency of these models based on five relevant metrics (TP Rate, FP rate, F-Measure, Precision, and accuracy) [23] [24].”
MethodA feature extraction model based on discriminative graph signals
Lin, W., Huang, J., Suen, C. Y., & Yang, L. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112861 · Cited by 16
Recommender system for health care analysis using machine learning technique: a review
Shaikh, S. G., Kumar, B. S., & Narang, G. (2022) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2022.2061078 · Cited by 17
“…for entity or product to be of use to a user.recommender Systems (RSs) helps in different decision-making processes, like ‘what items to purchase, what research article to read, or what type of application service to avail’ like travel recommender system (Portugal, Alencar, and Cowan 2018).”
BackgroundMethodInfluentialJob Recommendation System Using Hybrid Filtering
Mulay, A., Sutar, S., Patel, J., Chhabria, A., & Mumbaikar, S. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224402002 · Cited by 15
Robots and Artificial Intelligence in the Military.
Agarwala, N. (2023) · Obrana A Strategie (defence and Strategy) · doi:10.3849/1802-7199.23.2023.02.083-100 · Cited by 11
“…as for smart factory (Cioffi et al. 2020), smart logistics (Woschank et al. 2020), autoimmune diseases (Stafford et al. 2020), recommender systems (Portugal et al. 2018), supply chain management (Wenzel et al. 2019), to name a few, provide the requisite flavour of the expanse of the field and the…”
BackgroundLetard, A., Gutowski, N., Camp, O., & Amghar, T. (2024) · Expert systems with applications · doi:10.1016/j.eswa.2024.123151 · Cited by 18
Frid-Nielsen, S. S. (2019) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3298689.3346992 · Cited by 15
“Request permissions from permissions@acm.org. within the private sector to recommend media or other products to customers [19, 20].”
BackgroundPrediction of Phone Prices Using Machine Learning Techniques
Subhiksha, S., Thota, S., & Sangeetha, J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1097-7_65 · Cited by 15
Design and Implementation of a Computerized User Authentication System for E-Learning
Khan, F., & Alotaibi, S. R. (2020) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v15i09.12387 · Cited by 15
An Information Gain-Based Model and an Attention-Based RNN for Wearable Human Activity Recognition
Liu, L., He, J., Ren, K., Lungu, J., Hou, Y., & Dong, R. (2021) · Entropy · doi:10.3390/e23121635 · Cited by 16
“However, they need professional domain knowledge for manual feature extraction and complex preprocessing steps [26].”
BackgroundSamah, K. A. F. A., Misdan, N. F. A., Jono, M. N. H. H., & Riza, L. S. (2022) · JOIV International Journal on Informatics Visualization · doi:10.30630/joiv.6.1.879 · Cited by 15
“3) Naïve Bayes Classifier: Machine learning (ML) uses computers to simulate human understanding [19].”
BackgroundNing, Q. X., Wang, D., Cheng, F., Zhong, Y., Ding, Q., & You, J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04137-0 · Cited by 15
Schrank, J. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1649392 · Cited by 23
“Machine learning algorithms can analyze data to offer customized recommendations, financial insights, and predictive analytics (Portugal et al., 2018).”
Assessment of Investment Activity in the Regions
Krichevsky, M. L. (2020) · TEM Journal · doi:10.18421/tem93-02 · Cited by 14
“ML methods are now used in various fields, for example, in risk assessment management, in production, in the development of recommendation systems [7], [8], [9].”
MethodWu, J., & Zhou, Z. (2023) · Acta Geotechnica · doi:10.1007/s11440-023-01897-2 · Cited by 14
“In general, machine learning can allow computers to identify and acquire knowledge from the real world and improve performance of risk assessment tasks by using this new knowledge [16, 41].”
BackgroundJithendra, T., Khan, M. Z., Basha, S. S., Das, R., Divya, A. R., Chowdhary, C. L., Alahmadi, A. M., & Alahmadi, A. H. (2024) · IEEE Access · doi:10.1109/access.2024.3350642 · Cited by 14
“It provides a variety of commended services and features in addition to filling the gap between individuals and the internet [8].”
BackgroundIntelligent Crop Recommender System for Yield Prediction Using Machine Learning Strategy
Maheswary, A., Nagendram, S., Kiran, K. U., Ahammad, S. H., Priya, P. P., Hossain, M. A., & Rashed, A. N. Z. (2024) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-024-01029-8 · Cited by 14
“It uses ensemble-based-knowledge theory [7, 21].”
A Report: Machine Learning and Its Applications
Ankita, Mittal, S., Sharma, I., & Kumar, A. (2023) · International Conference on Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) · doi:10.1109/iccebs58601.2023.10448693 · Cited by 14
“…Engine: while searching for any content we get more recommendations as same to the product we are searching for, After the search mobile’s different software shows the results in the feed this is by using the recommender system, this system helps the user to find the things more easily [30][31].”
Social book search: the impact of the social web on book retrieval and recommendation
Ullah, I., & Khusro, S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08591-0 · Cited by 14
Choi, Y.-H., Lee, J., & Yang, J. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116084 · Cited by 14
Proposed Model to Intelligent Recommendation System based on Markov Chains and Grouping of Genres
Mlika, F., & Karoui, W. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.09.082 · Cited by 14
Personalized Recommendation System Design for Library Resources through Deep Belief Networks
Liu, M. (2022) · Mobile Information Systems · doi:10.1155/2022/7870724 · Cited by 13
RetractedZhou, N., Hu, T., Wu, M., Chen, Q., & Qi, C. (2024) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2024.113328 · Cited by 13
Recommendation and Classification Systems: A Systematic Mapping Study
Enríquez, J. G., Morales-Trujillo, L., Calle-Alonso, F., Mayo, F. J. D., & Lucas-Rodríguez, J. M. (2019) · Scientific Programming · doi:10.1155/2019/8043905 · Cited by 13
“…of associative classifiers in order to alleviate typical drawbacks in recommender systems [88] S3Mining: a model-driven engineering approach for supporting novice data miners in selecting suitable classifiers [89] The use of machine learning algorithms in recommender systems: a systematic review [11]”
BackgroundBarros, M., Moitinho, A., & Couto, F. M. (2019) · IEEE Access · doi:10.1109/access.2019.2958002 · Cited by 13
The Tourist Attractions Recommender System for Bangkok Thailand
Chujai, P., Singthongchai, J., Yasaga, S., Suratthara, N., & Buranakutti, K. (2020) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2020.v12.1258 · Cited by 13
Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data
Rohrmanstorfer, S., Komarov, M. M., & Mödritscher, F. (2021) · Mathematics · doi:10.3390/math9060624 · Cited by 13
“Computers are presented with real-world data without the corresponding correct output values and must learn from that data on their own [8].”
BackgroundA hybrid group-based movie recommendation framework with overlapping memberships
Ali, Y., Khalid, O., Khan, I. A., Hussain, S. S., Rehman, F. U., Siraj, S., & Nawaz, R. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266103 · Cited by 13
“In literature, researchers proposed various clustering methods to form groups [64].”
BackgroundA decision analysis approach for selecting software defect prediction method in the early phases
Özakıncı, R., & Tarhan, A. (2022) · Software Quality Journal · doi:10.1007/s11219-022-09595-0 · Cited by 13
“– Performance: Performance is the degree of which the method performs well in general (Portugal et al., 2018).”
MethodAattouri, I., Mouncif, H., & Rida, M. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i2.pp943-955 · Cited by 13
“The paper [23] gives a systematic review of MLA in recommender systems which can help application developers to deal with the algorithms, their types, and trends in the use of specific algorithms.”
BackgroundImproving Children’s Experience on a Mobile EdTech Platform through a Recommender System
Ruiz‐Iniesta, A., Melgar, L., Baldominos, A., & Quintana, D. (2018) · Mobile Information Systems · doi:10.1155/2018/1374017 · Cited by 13
Ensemble-based Top-k Recommender System Considering Incomplete Data
Moradi, M., & Hamidzadeh, J. (2019) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22044/jadm.2019.7026.1830 · Cited by 13
“Hence, the majority of studies in recommender systems field use data mining techniques [12].”
BackgroundErsöz, F., & ÇINAR, Y. (2021) · European Journal of Science and Technology · doi:10.31590/ejosat.1035124 · Cited by 12
Clus-DR: Cluster-based pre-trained model for diverse recommendation generation
Yadav, N., Pal, S., Singh, A. K., & Singh, K. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.02.010 · Cited by 12
Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review
Shahmoradi, L., Azadbakht, L., Farzi, J., Kalhori, S. R. N., Yazdipour, A. B., & Solat, F. (2024) · BMC Women s Health · doi:10.1186/s12905-024-03074-3 · Cited by 15
“The findings of the study highlighted various machine learning techniques and their applications, as well as primary and alternative performance criteria [13].”
BackgroundPT Symmetry-Enabled Physically Unclonable Functions for Anti-Counterfeiting RF Tags
Ren, Y., Yang, M., Pan, H., Farhat, M., Çetin, A. E., & Chen, P. (2024) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2024.3396693 · Cited by 13
“…recent years, the emergence of machine learning (ML) techniques has offered significant potential for aiding in the domains of image and speech recognition [41], [42], [43], nat-ural language processing [44], recommendation systems [45], and inverse design within the field of electromagnetics [46].”
BackgroundFarshidi, S., Rezaee, K., Mazaheri, S., Rahimi, A. H., Dadashzadeh, A., Ziabakhsh, M., Eskandari, S., & Jansen, S. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09398-x · Cited by 14
“Consequently, in the realm of intent modeling approaches, the following research challenges have been identified: Scattered knowledge: The concepts, models, and characteristics of intent modeling approaches are dispersed across diverse academic literature [40], hindering informed decision-making for developing convers…”
InfluentialImproved Ranking Based Collaborative Filtering Using SVD and Borda Algorithm
Ardiansyah, M. I., Adji, T. B., & Setiawan, N. A. (2019) · International Conference of Artificial Intelligence and Information Technology (ICAIIT) · doi:10.1109/icaiit.2019.8834597 · Cited by 12
“This dataset is popular among recommendation system researches [10].”
BackgroundMethodLsRec: Large-scale social recommendation with online update
Zhou, W., Zhou, Y., Li, J., & Memon, M. H. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113739 · Cited by 12
A Review on Movie Recommendation System Using Machine Learning
Smitha, N., Anusha, D., Chaithanya, C. P., Sindhu, J., Tanuja, R., & Kumar, H. (2021) · International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · doi:10.1109/icicv50876.2021.9388619 · Cited by 12
“Using Machine learning for this purpose solves the problem [2].”
BackgroundSystematic analysis of Movie Recommendation System through Sentiment Analysis
Lavanya, R., & Bharathi, B. (2021) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais50930.2021.9395854 · Cited by 12
“It was observed that SVD++ Algorithm is the one that takes time Perception into consideration [7] .”
BackgroundHybrid Filtering Recommendation System in an Educational Context
Baidada, M., Mansouri, K., & Poirier, F. (2021) · International Journal of Web-Based Learning and Teaching Technologies · doi:10.4018/ijwltt.294573 · Cited by 12
“To evaluate our referral system, we have based ourselves on the three main indicators used in these contexts ( Burke , 2007; Isinkaye et al., 2015; Portugal et al., 2017; Roy et al., 2010), namely: • Precision: the number of recommended resources (like) found relative to the total number of…”
The UbiCARS Model-Driven Framework: Automating Development of Recommender Systems for Commerce
Mettouris, C., Achilleos, A., Kapitsaki, G. M., & Papadopoulos, G. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03062-9_3 · Cited by 12
“In addition, due to the substantial number of ML algorithms and their variations proposed in the literature, a clear classification scheme for them does not exist [9], making it even more difficult for practitioners to select the ML algorithm that best fits their needs when developing a RS.”
BackgroundInfluentialPark, Y., & MacDonald, G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902937116 · Cited by 12
Intelligent Mobile-Based Recommender System Framework for Smart Freight Transport
Gheraibia, M. Y., & Gouin-Vallerand, C. (2019) · EAI International Conference on Smart Objects and Technologies for Social Good (GOODTECHS) · doi:10.1145/3342428.3342697 · Cited by 12
“We can summarize the PCA into five tasks, suppose that we have a dataset F with d dimensions [3]:”
MethodScholarly publication venue recommender systems
Dehdarirad, H., Ghazimirsaeid, J., & Jalalimanesh, A. (2020) · Data Technologies and Applications · doi:10.1108/dta-08-2019-0135 · Cited by 12
Ismail, W., Al-Hadi, I. A. A.-Q., Groşan, C., & Hendradi, R. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.599 · Cited by 12
A Review on Vision-based Hand Gesture Recognition Targeting RGB-Depth Sensors
Rawat, P. S., Kane, L., Goswami, M., Jindal, A., & Sehgal, S. (2022) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622022300026 · Cited by 12
Deep Learning for Automated Recognition of Covid-19 from Chest X-ray ImagesPreprint
Duong, L. T., Nguyen, P. T., Iovino, L., & Flammini, M. (2020) · medRxiv · doi:10.1101/2020.08.13.20173997 · Cited by 12
A Survey of Recommender Systems Based on Semi-supervised Learning
Khan, A. H., Siddqui, J., & Sohail, S. S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-3071-2_27 · Cited by 11
Mani, V., & Thilagamani, S. (2023) · International Journal of Computers Communications & Control · doi:10.15837/ijccc.2023.6.5086 · Cited by 11
A Hybrid Group-Based Food Recommender Framework for Handling Overlapping Memberships
Rais, R. N. B., Noman, M., Khalid, O., & Khan, I. A. (2024) · Applied Sciences · doi:10.3390/app14135843 · Cited by 11
Al-Nouti, A. F., & Yaseen, Z. M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111511 · Cited by 11
Devi, K. R., & Bhavithra, J. (2020) · International journal of scientific and technology research · Cited by 11
A rule-based automated machine learning approach in the evaluation of recommender engine
Behera, R. K., Bala, P. K., & Jain, R. (2020) · Benchmarking An International Journal · doi:10.1108/bij-01-2020-0051 · Cited by 11
Mu, W., Feng, Y., Shu, H., Wang, B., & Tian, D. (2022) · Italian Journal of Food Science · doi:10.15586/ijfs.v34i2.2209 · Cited by 11
“…best recommendation models of each subtask were screened scholars have introduced machine learning technology into recommendation algorithms (Zhang and Lei, 2021), which mainly focuses on using supervised learning algorithms to predict user preferences in different fields (Portugal et al., 2018).”
BackgroundSenthilselvan, N., Subramaniyaswamy, V., Kotecha, K. V., Indragandhi, V., Palani, S., Saini, J. R., & Ravi, L. (2022) · IEEE Access · doi:10.1109/access.2022.3175566 · Cited by 11
“For example, several researchers import the user-defined neighborhood and user similarity calculated in the original area to the target domain [17].”
BackgroundPublication Venue Recommendation Using Profiles Based on Clustering
de Campos, L. M., Fernández-Luna, J. M., & Huete, J. F. (2022) · IEEE Access · doi:10.1109/access.2022.3212531 · Cited by 11
An approach for proactive mobile recommendations based on user-defined rules
Ilarri, S., & Trillo-Lado, R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122714 · Cited by 11
Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging
Harrison, R. M., Dereventsov, A., & Bibin, A. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00195 · Cited by 14
“Leveraging vast amounts of user and content data via intricate algorithms, machine learning enables these systems to discern intricate patterns and preferences that traditional rule-based approaches would struggle to capture, see [14], [17], [25].”
BackgroundKim, N.-R., Oh, S., & Lee, J.-H. (2018) · Applied Sciences · doi:10.3390/app8081323 · Cited by 11
“The recommender system has been recognized as an important issue in the data mining field and actively researched over decades [ 1–29].”
BackgroundComplementary Recommendations: A Brief Survey
Hang, Y., Litchfield, L., Kernreiter, T., Jolly, S., & Hempstalk, K. (2019) · International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS) · doi:10.1109/hpbdis.2019.8735479 · Cited by 11
“In comparison with other surveys that mainly focus on substitute recommendations [4]–[9], we aim to contribute”
ResultUnavailable Transit Feed Specification: Making It Available With Recurrent Neural Networks
Iovino, L., Nguyen, P. T., Salle, A. D., Gallo, F., & Flammini, M. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3053373 · Cited by 12
“Such algorithms are capable of simulating humans’ learning activities, aiming to acquire real-world knowledge autonomously [4] by generalizing from concrete examples [5], [6].”
BackgroundDeploying a co-training algorithm to classify human-rights abuses
Gokhale, R., & Fasli, M. (2017) · International Conference on the Frontiers and Advances in Data Science (FADS) · doi:10.1109/fads.2017.8253206 · Cited by 11
“Automatic domain detection and classification in text has been researched and several tools have been proposed using various classifiers and heuristics [16].”
MethodDesign and Evaluation of a Smart Medication Recommendation System for the Electronic Prescription.
Zaveisadat, S. H. G., Etminani, K., Dehghan, H., Eslami, S., Hasibian, M. R., Vakili-Arki, H., Saberi, M. R., Aghabagheri, M., & Namayandeh, S. M. (2019) · PubMed · Cited by 11
Style conditioned recommendations
Iqbal, M., Aryafar, K., & Anderton, T. (2019) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3298689.3347007 · Cited by 11
“Variational Autoencoders (VAEs) are a natural choice for recommendations as they can rapidly perform collaborative filtering and impute missing values in user item interaction data, one of the most popular recommendation methodologies [22].”
BackgroundDLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud
Wang, Q., Lan, T., Tang, Y., Sang, B., Huang, Z., Du, Y., Zhang, H., Sha, J., Lu, H., Zhou, Y., et al. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3685800.3685832 · Cited by 12
“Deep learning based recommendation models (DLRM) are prevalent in recommendation scenarios [18, 26, 33, 46, 51, 79].”
BackgroundA Comparative Evaluation of Recommender Systems Tools
Akhadam, A., Kbibchi, O., Mekouar, L., & Iraqi, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3541014 · Cited by 10
“Portugal et al. [22] conduct a systematic review of the literature to investigate the use of ML algorithms in RSs.”
Artificial Intelligence in Project Success: A Systematic Literature Review
Su, X., & Ayob, A. H. (2025) · Information · doi:10.3390/info16080682 · Cited by 13
Towards Interpretability of the Movie Recommender Based on a Neuro-Fuzzy Approach
Rutkowski, T., Romanowski, J., Woldan, P., Staszewski, P., & Nielek, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91262-2_66 · Cited by 10
Recommender System with Artificial Intelligence for Fitness Assistance System
Tran, T. T., Choi, J. W., Dang, C. V., SuPark, G., Baek, J. Y., & Kim, J. W. (2018) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/urai.2018.8441895 · Cited by 10
Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis
Nazeer, I., Rashid, M. U., Gupta, S. K., & Kumar, A. (2020) · Advances in social networking and online communities book series · doi:10.4018/978-1-7998-4718-2.ch002 · Cited by 10
Machine Learning Technique for Wireless Sensor Networks
Kaur, R., Sandhu, J. K., & Sapra, L. (2020) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc50313.2020.9315775 · Cited by 10
“ML is divided into Supervised Learning, Unsupervised Learning, and Reinforcement Learning [6].”
MethodBarley Variety Identification by iPhone Images and Deep Learning
Shi, Y., Patel, Y., Rostami, B., Chen, H., Wu, L., Yu, Z., & Li, Y. (2021) · Journal of the American Society of Brewing Chemists · doi:10.1080/03610470.2021.1958602 · Cited by 10
“[10] Its main aim is to make computers learn automatically from experience or data consisting of features and ground truth.”
BackgroundDeep learning approach to obtain collaborative filtering neighborhoods
Bobadilla, J., González-Prieto, Á., Ortega, F., & Lara-Cabrera, R. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06493-7 · Cited by 10
Yaman, T. T., & Başeğmez, H. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16598-6_15 · Cited by 10
A systematic review of machine learning techniques in online learning platforms
Agbewali-Koku, C. E. K., Rahman, M. A., Hamada, M., Ali, M. A., Oysharja, L. N., & Hossain, M. T. (2022) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc57363.2022.00046 · Cited by 10
Türkiye’de ikinci el araçların büyük veri ve makine öğrenme teknikleriyle analizi ve fiyat tahmini
Gülmez, B., & Kulluk, S. (2023) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.980840 · Cited by 11
Ismail, Z.-A. (2023) · Open House International · doi:10.1108/ohi-03-2023-0065 · Cited by 10
Automated Machine Learning for Information Retrieval in Scientific Articles
Rakhshani, H., Latard, B., Brévilliers, M., Weber, J., Lepagnot, J., Forestier, G., Hassenforder, M., & Idoumghar, L. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185893 · Cited by 10
“In recent years, a number of surveys have been presented to provide a review of related works on ML-based recommendation systems [9], [10], [11], [12].”
BackgroundChen, J., Lu, Y., Shang, F., & Zhu, T. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104191 · Cited by 10
A hinge-loss based codebook transfer for cross-domain recommendation with non-overlapping data
Veeramachaneni, S. D., Pujari, A. K., Padmanabhan, V., & Kumar, V. (2022) · Information Systems · doi:10.1016/j.is.2022.102002 · Cited by 10
Construction planning with machine learning
Shayboun, M., Kifokeris, D., & Koch, C. (2019) · Chalmers Research (Chalmers University of Technology) · Cited by 9
Because You Watched: How Do Streaming Services’ Recommender Systems Influence Aesthetic Choice?
Chapman, H. E., & Abraham, A. (2025) · Behavioral Sciences · doi:10.3390/bs15111544 · Cited by 11
“Machine learning is a type of artificial intelligence where computers simulate “human learning” through knowledge acquisition from real-world data and then improve performance from their knowledge (Portugal et al., 2018, p. 206).”
Semantic Web Science and Real-World Applications
Figueroa, C., Vagliano, I., Rocha, O. R., Torchiano, M., Zucker, C. F., Corrales, J. C., & Morisio, M. (2018) · Advances in web technologies and engineering book series · doi:10.4018/978-1-5225-7186-5 · Cited by 9
Performance Evaluation of Ensemble based Collaborative Filtering Recommender System
Venil, P., Vinodhini, G. A. F., & Suban, R. (2019) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan.2019.8878777 · Cited by 9
“[11].”
BackgroundMethodInfluentialHuman Activity Recognition Using Time Series Pattern Recognition Model-Based on Tsfresh Features
Luqian, S., & Yuyuan, Z. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498859 · Cited by 9
A recommender system for occupational hygiene services using natural language processing
Barrera, N., Torres, R., Rodríguez, J., Espinosa, O., Avellaneda, S., & Ramírez, J. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100148 · Cited by 9
kaheni, H., Shiran, M. B., Kamrava, S. K., & Sadeghi, A. Z. (2024) · Journal of Neuroradiology · doi:10.1016/j.neurad.2024.02.006 · Cited by 9
Yücebaş, S. C. (2019) · Journal of Advanced Research in Natural and Applied Sciences · doi:10.28979/comufbed.597093 · Cited by 9
Big Data Classification: Applications and Challenges
Pramanik, P. K. D., Mukhopadhyay, M., & Pal, S. (2021) · Studies in big data · doi:10.1007/978-981-33-6400-4_4 · Cited by 9
Aslam, F. (2023) · Journal of Engineering Research and Reports · doi:10.9734/jerr/2023/v25i7949 · Cited by 9
Bsoul, Q., Zawaideh, F., Alqadi, B. S., Almusfar, L. A., Khalaf, O. I., Alattas, A. S., Alali, M., & AbdElminaam, D. S. (2025) · SN Computer Science · doi:10.1007/s42979-025-03742-7 · Cited by 8
Zhang, D., Liu, J., Wang, H., & Wang, Y. (2025) · Environmental Research · doi:10.1016/j.envres.2025.123449 · Cited by 8
The Influence of Reviewer and Consumer Congruence in Online Word-of-Mouth Transactions
Furner, C. P., Yoon, T. E., Zinko, R., & Goh, S. H. (2021) · Journal of Electronic Commerce in Organizations · doi:10.4018/jeco.2021070101 · Cited by 10
TN-GAN-Based Pet Behavior Prediction through Multiple-Dimension Time-Series Augmentation
Kim, H., & Moon, N. (2023) · Sensors · doi:10.3390/s23084157 · Cited by 8
“A machine learning algorithm has been used to achieve human activity recognition [22,23].”
MethodCentroid Distance Distillation for Effective Rehearsal in Continual Learning
Liu, D., Lyu, F., Li, L., Xia, Z., & Hu, F. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094837 · Cited by 8
“Continual Learning (CL) is used to enable a machine learning system to learn from a sequence of tasks like humans [1], which has been applied to many applications, such as the recommender systems [2], medical research [3, 4] and clinical management decisions [5].”
MethodLASGRec: A Personalized Recommender Based on Learnable Attribute Sampling and Graph Neural Network
Wang, Y., Huang, X., Ma, J., & Jin, Q. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3311433 · Cited by 8
“Among the existing recommender systems, collaborative filtering (CF) is one of the most popular techniques, which models users’ preferences for items through utilizing the historically available user–item (UI) interactions [3], [4], [5].”
Background
Metadata
- Authors
- Portugal, I., Alencar, P., & Cowan, D.
- Affiliations
- University of Waterloo (CA)
- Corresponding author
- Portugal, Ivens
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 97
- Pages
- 205-227
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2263972221
- Cited by
- 743
- References
- 118
- Citations by year
- 2026: 34 · 2025: 81 · 2024: 97 · 2023: 95 · 2022: 106 · 2021: 113 · 2020: 104 · 2019: 69 · 2018: 36
- FWCI
- 18.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Expert Systems with Applications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UWSpace (University of Waterloo) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- NSERC
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- recommender systems, machine learning algorithms, algorithm selection, big data technologies, performance indicators, research trends, research opportunities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
